One-Class Learning Weed Plants Detection on Multispectral Images

Modern precision agriculture methods focus on efficient crop care procedures with targeted chemical applications. As current computer vision algorithms allow us to distinguish different plant species, spot-spraying systems for precise herbicide application only on weed plants are being developed and used in praxis. Many such systems rely on deep learning algorithms trained on large datasets containing all possible plant species. While the crop plants keep the same visual characteristics all around the world, the species composition of weed plants can differ significantly, leading to lower detection accuracy for weed species that are not represented in the training dataset. In this work, we apply the PatchCore and PaDiM, two state-of-the-art anomaly detection algorithms, to a multispectral dataset of corn plant images in a one-class learning paradigm. The best performing algorithm achieved AUROC 94.2 despite the high visual heterogeneity and scarcity of the input data. Our results suggest it is possible to train weed-detection algorithms on a limited dataset in a one-class learning setting to transform the species classification into an anomaly detection problem.